Systems, processes, methods and machines for transforming image data into sizing and volume measurements for tissue

ABSTRACT

Automated islet measurement systems (AIMS) in combination with tissue volume analysis (TVA) software effectively gauges volumetric and size-based data to generate heretofore unavailable information regarding, for example, populations of islet cells, stem cells and related desiderata.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the full Paris Convention benefit to andpriority of U.S. Provisional Application Ser. No. 61/720,153, filed Oct.30, 2012, the contents of which are incorporated by this reference as iffully set forth herein in their entirety.

BACKGROUND OF THE DISCLOSURE

Various tissue elements and other biological matter require precise datato support characterization, culturing and rendering the same effect forimportant usages and purposes. Optical imagery likewise provides apowerful tool.

Those skilled in the art readily understand that a first step inmanaging sensitive tissue can be found by capturing image data andarraying the same for various purposes.

According to the instant disclosures, scanning imagery technology,improved algorithms and alternate optical techniques may be used toinventively gauge, measure and validate aspects of tissue measurement,by volume.

OBJECTS AND SUMMARY OF THE DISCLOSURE

Briefly stated, automated islet measurement systems (AIMS) incombination with tissue volume analysis (TVA) software effectivelygauges volumetric and size-based data to generate heretofore unavailableinformation regarding, for example, populations of islet cells, stemcells and related desiderata.

According to embodiments, there is disclosed an automatic tissuescanning camera (ATSC) system and methods to measure sizes and volumesof, for example, populations of islet cells within a culture flask.

According to embodiments, there is disclosed a system comprising tissuevolume analysis (TVA) software, using thresholding to identifyindividual cells, for example islet cells, and to evaluatecross-sectional linear size of cells, for example islet cells, via analgorithmic simulacrum of manual counting under microscopy.

According to embodiments, there is disclosed a system, wherein said TVAsoftware measures the optical transmission of each pixel within eachcell, for example islet cell, using this to calculate thickness oftissue at that pixel based upon optical extinction; and, based onconstant pixel area, calculates volume represented by respective pixels;whereby voxels, volume elements, are then summed to generate resultorytotal cell, for example islet cell, volume.

According to embodiments, there is disclosed a system wherein anautomated image measurement algorithm, as defined in Appendix A, isemployed.

According to embodiments, there is disclosed a process for generatingsizing and volume measurement data by transforming optical imagery,comprising, in combination: providing an AIMS/imaging apparatus/scanner;having TVA algorithms and related software disposed therein; imaging atleast a cell; generating predetermined fields for sizing and volume;conforming, registering and (or otherwise validating) the resultorydata; and, using the same to create information which can be furthercompared to laser transaction microscopy and diffusing measurement datato confirm resulting data sets.

According to embodiments, there is disclosed a machine for transformingoptical data into cellular sizing and volume measurement information,whereby scanned images are assembled further comprising at least one ofislets and spheres of stem cells; and, in an independent unit validatingthe same, whereby confocal microscopy, likewise capable of diffusingmeasure, enables optical slicing of images of said islets and spheres ofstem cells horizontally, using scattered light to document the subjectarea, which multiplied by thickness generates an independent validationof the volume generated by said machine.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic block/flow diagram showing operation of systems,methods, processes and products by the processes, according to theinstant disclosures; and

FIG. 2 is a table distribution into depth X of permeant in a thinmembrane.

DETAILED DESCRIPTION OF THE DISCLOSURE

Expressly incorporated herein by reference are U.S. Pat. Nos. 8,211,697;8,213,081; 8,228,499; 8,293,223; 8,236,281; and 8,288,339.

The present inventor has discovered how to use new combinations ofoptical tools to characterize heretofore un-cognizable configurations ofcells, constituents thereof and related desiderata having extensiveimplications.

Referring now to FIG. 1 and FIG. 2, algorithms embedded within softwareare accessed (including by remote, wireless and other means known ordeveloped) to drive AIMS/imaging system/scanner 103, which generatesdata 111 regarding sizing and volume of subject cells, for example,islet cells (as described in Appendices A and B, for example).

Said data 111 is useful for characterizing the physical nature ofsubject cells, in this case, islet cells, whose spatial orientation andrelated behavior can now be understood as driven by data 111 andconfirmed and registered with validated information—such as the moreellipsoid nature of the islet cells. Likewise, tissue transectionmicroscopy 107 and diffusing measurement technologies, can be used toconfirm these findings.

The Volume Measurement Algorithm for Individual Islets: This is a briefsummary of the Automated Image Measurement Algorithm for measurement oftissue volumes. This system is based on the following considerations.

The volume of a right parallelepiped may be expressed as: V=A×h

The volume of an arbitrary object can be approximated by summing thediscrete element volumes of the small parallelepipeds with uniformcross-section area, A, and height, h, that compose it: V=Σ(A×h)

The digital image of an object is a construction of rectangles whoseindividual areas in the image are from individual pixels eachrepresenting a specific area in object space (dependent on, forinstance, “magnification”).

For absorption microscopy of biological tissues (i.e. non-phase contrastmicroscopy) without the use of stains, the 20 image corresponds to theoptical “shadow” cross-section. The individual pixel measurements are afunction of the thickness and the optical absorption of the tissue alongthe optical axis (this approximation is valid for thin sections) asdescribed by the Lambert-Beers Law: Ip=10 exp (−a×tp) where Ip is theintensity measured by a pixel imaging tissue, 10 is the intensity ofillumination and tp is the tissue thickness. Local optical transmissioncan be defined as T_(P)=I_(P)/1₀ so that T_(P)=(−In T_(P))/α; whereα=absorption coefficient.

Since the volume of an object can be represented by: V₀=Σ(A_(p)×t_(p));where the area and thickness are for individual pixels representing theobject; space, the volume may be calculated from the digital opticalimage of the object: V₀=Σ(A_(p)×(−InT_(p))/α).V₀=Σ(A_(p)×{−In(I_(p)/I₀)}/α).

Uncertainty Analysis—Individual Volumes: The uncertainty of the area ofan individual pixel in object space is dependent on the uncertainty ofmeasurement of the microscope field size. This uncertainty is less than2% (20 microns in a 3 mm field size). Σ(A_(p)×t_(p))

Thresholding: Objects are identified by binary thresholding so thesummation uncertainty is dependent on thresholding: only pixels that arebelow a threshold intensity are identified as part of an object(particle detection) therefore exclusion of light areas in objectsundercounts pixels in the summation. A binary closing function fillslight areas identified as “holes” in objects so this defect will onlyaffect thin portions surrounding the outer fringe of identified objects.The effect of thresholding has been evaluated functionally by measuringobjects with thresholds set significantly above and below optimal (>6units in approximately 130). The variation of measured volume betweenthreshold extremes and optimal is less than 10%.

Calibration to micro-spheres: We have assessed particle detection andmeasurement reliability by comparing particle measurements with theimage analysis system to NIST-traceable specifications for CoulterCounter calibrating spheres. The measurement of particle size (area)using image analysis compares very closely with the specifications forcalibrating spheres.

T_(P)=I_(p)/I₀—measurement of light intensity: Light intensitymeasurement with CCDs: CCD pixel outputs are individually linear overmany decades of illumination intensity. We correct for CCDpixel-to-pixel sensitivity and local variations of illumination andviewing response by flat-field image processing on all images prior toextraction of digital information. The flat field image, reflecting theeffects of illumination, optics and the CCD, has a typical standarddeviation of approximately 3% (6 parts in 200). After flat fieldcorrection this non-uniformity is reduced to virtually zero.

Test of linearity: Linearity has been confirmed (by use of overlappingcalibrated density filters) to be on average within +/−2% over the rangeof intensity measurements of interest.

Algorithm for measuring transmission: The measurement of backgroundintensity, I₀, is based on averaging the measured intensity of light ina ring around each identified object. The ring is created by a series of3-pixel wide object dilations followed by image subtraction. Errors inthis measurement can arise if any field darkening, in particular by anadjacent particle, lies close enough to be within the ring. Our objectimage density is low (<1%) so this has not been observed to be aproblem. This technique for measuring background minimizes the effect oflight intensity variations locally per image and over the time of imageacquisition.

Effect of focus: The accurate measurement of intensity by microscope isaffected by focussing. Objects above or below the focal plane lose“contrast”, that is, approach the brightness of the background. We havemeasured the effect of placing the focus above and below object tissuemasses, that is, above and below the nominally perceived best-focusposition. The worst-case out-of-focus condition changes measured tissuemass by less than 10%. Since tissue typically settles rapidly to thebottom of the flask, this effect has the beneficial effect of helping todiscriminate against out-of-focus non-tissue image artifacts such asfloating fragments or outer flask wall defects; the threshold eliminateslightened artifacts.

Measurement of the absorption coefficient: The optical darkening thatmakes an object tissue visible in non-phase contrast microscopy is dueto absorption of chromophores native to the object. For thin sections,where scattering is negligible, the average absorption is describable bythe absorption coefficient, a. We have measured an average a for islettissues and for NIT cells by measuring transmission of tissue at 650 nmcaptured in known spaces between microscope slides. The coefficientmeasured varies between 0.0033 um-1 and 0.0051 um-1 (see attached figure“measurement of absorption coefficient”). These numbers comparefavorably to values in the literature for other tissues at thiswavelength.

Specific value: For calculation of volume we have adopted the averagevalue of 0.0033 um-1. For detection of relative changes in tissue volumeover time, this consistent use of a single value is required.

Use of average absorption coefficient: The absorption coefficient variesfor specific morphological features of a tissue. To minimize this effectwe have chosen monochromatic, long-wavelength, visible light forillumination because generally the absorption coefficient for biologicaltissues decreases and converges for longer red wavelengths.

To evaluate the use of the average absorption coefficient algorithm, wesqueezed NIT cells between microscope slides separated by rigid spheresof known size. The consequently flat, parallel-sided tissue disks weremeasured for volume by optical imaging. This was compared to calculationof volume as area times thickness. The regression (see figure “testalgorithm”) was essentially a straight line, which confirms the use ofan average coefficient for thickness calculation.

Test of volume measurement: The ability to accurately measure the volumeof a single object was tested by imaging a plate-like porcine isletmanipulated to edge-on and face-on positions. The difference inthicknesses presented by the two different views is a test of thevalidity of our optical transmission thickness measurement algorithm.Linear dimensions measured using the microscope image versustransmission were within 4% of each other and volumes measured werewithin 6% of each other (see attached figure “camps by view” andcorresponding images).

Overall uncertainty of individual islet volume measurement: Based on theabove the uncertainty of measurement of relative individual isletvolumes is estimated to be less than 15%. The estimate of absolutevolume measurement is dependent on further characterization of theabsorption coefficient.

A flask containing a population of particles may be characterized byoptical sampling. Since all the islets quickly settle to the bottom ofour flasks, we can obtain an accurate sample of the population byimaging as much of the flask area as possible. The accuracy of thissampling is governed by sampling statistics where our population isnon-homogeneous in particle size. The volume characteristics of thepopulation can be calculated: V_(K)=V_(Ks)×S_(K).

Where V_(K) is the volume (or number) of islets in a histogram size binK and V_(Ks) is the volume (or number) measured by optical sampling andS_(K) is the sample multiplier for a given bin. The sample multiplieris: S_(K)=1/P=A_(F)/(A_(im)×m).

Where P is the fraction of the bottom of the flask which is sample byimages, A_(F) is the area of the flask bottom and A_(im) is theobject-area of one image and m is the number of non-overlapping imagestaken. For our work P has been between 0.05 and 0.40 (currently about10% sampling). The total volume in a flask is calculated: V_(T)=ΣV_(K).

The uncertainty of sample analysis is based on sampling statistics. Theuncertainty of a bin sample is inversely proportional to the square rootof n, where n is the number of particles in a given bin. We have testedthe sampling variations by measuring several times in succession a flaskcontaining microspheres. The average fractional range (maximum tominimum divided by the average) for eight series was 6.3%.

We have compared the measurement results of image analysis particlecounting to results with the Coulter Counter using identical samples ofcalibrated microspheres. The image analysis count was approximately 15%low for 68 micron diameter spheres and was identical for 200 micronspheres (see attached figure “compare Coulter to IA beads”). We havetested the sampling variations for tissue by measuring several times insuccession a flask containing human islets. Preliminary results showsimilar consistency, although, as expected, the sampling uncertaintyincreases for larger size particles because the population contains everfewer large volume particles.

Artisans readily understand the nature and extent of the instant systemto be useful for stem cells and related desiderata.

Returning to islet cells, it has long been assumed that a generallyspherical configuration was in play, impacting numerous empiricalaspects of their storage, usage, and other key issues. Using theteachings of the present disclosure, the ellipsoid (or, American“football-like”) shape of islet cells was confirmed, enabling ongoingresearch heretofore uncontemplated as currently understood. Suchteachings comprise progress in science and the useful arts, and likewisepresent subject matters for U.S. Letters Patents.

While the method and apparatus have been described in terms of what arepresently considered to be the most practical and preferred embodiments,it is to be understood that the disclosure need not be limited to thedisclosed embodiments. It is intended to cover various modifications andsimilar arrangements included within the spirit and scope of the claims,the scope of which should be accorded the broadest interpretation so asto encompass all such modifications and similar structures. The presentdisclosure includes any and all embodiments of the following claims.

It should also be understood that a variety of changes may be madewithout departing from the essence of the invention. Such changes arealso implicitly included in the description. They still fall within thescope of this invention. It should be understood that this disclosure isintended to yield a patent covering numerous aspects of the inventionboth independently and as an overall system and in both method andapparatus modes.

Further, each of the various elements of the invention and claims mayalso be achieved in a variety of manners. This disclosure should beunderstood to encompass each such variation, be it a variation of anembodiment of any apparatus embodiment, a method or process embodiment,or even merely a variation of any element of these.

Particularly, it should be understood that as the disclosure relates toelements of the invention, the words for each element may be expressedby equivalent apparatus terms or method terms—even if only the functionor result is the same.

Such equivalent, broader, or even more generic terms should beconsidered to be encompassed in the description of each element oraction. Such terms can be substituted where desired to make explicit theimplicitly broad coverage to which this invention is entitled.

It should be understood that all actions may be expressed as a means fortaking that action or as an element which causes that action.

Similarly, each physical element disclosed should be understood toencompass a disclosure of the action which that physical elementfacilitates.

Any patents, publications, or other references mentioned in thisapplication for patent are hereby incorporated by reference. Inaddition, as to each term used it should be understood that unless itsutilization in this application is inconsistent with suchinterpretation, common dictionary definitions should be understood asincorporated for each term and all definitions, alternative terms, andsynonyms such as contained in at least one of a standard technicaldictionary recognized by artisans and the Merriam-Webster's UnabridgedDictionary, the latest edition of which is hereby incorporated byreference.

Finally, all references listed in the Information Disclosure Statementor other information statement filed with the application are herebyappended and hereby incorporated by reference; however, as to each ofthe above, to the extent that such information or statementsincorporated by reference might be considered inconsistent with thepatenting of this/these invention(s), such statements are expressly notto be considered as made by the applicant.

In this regard it should be understood that for practical reasons and soas to avoid adding potentially hundreds of claims, the applicant haspresented claims with initial dependencies only.

Support should be understood to exist to the degree required under newmatter laws—including but not limited to United States Patent Law 35 USC132 or other such laws—to permit the addition of any of the variousdependencies or other elements presented under one independent claim orconcept as dependencies or elements under any other independent claim orconcept.

To the extent that insubstantial substitutes are made, to the extentthat the applicant did not in fact draft any claim so as to literallyencompass any particular embodiment, and to the extent otherwiseapplicable, the applicant should not be understood to have in any wayintended to or actually relinquished such coverage as the applicantsimply may not have been able to anticipate all eventualities; oneskilled in the art, should not be reasonably expected to have drafted aclaim that would have literally encompassed such alternativeembodiments.

Further, the use of the transitional phrase “comprising” is used tomaintain the “open-end” claims herein, according to traditional claiminterpretation. Thus, unless the context requires otherwise, it shouldbe understood that the term “compromise” or variations such as“comprises” or “comprising”, are intended to imply the inclusion of astated element or step or group of elements or steps but not theexclusion of any other element or step or group of elements or steps.

Such terms should be interpreted in their most expansive forms so as toafford the applicant the broadest coverage legally permissible.

1. An automatic tissue scanning camera (ATSC) system, which comprises,in combination: a monochrome CCD camera; a horizontally-mounted cultureflask; trans-illumination means, which means exceeds the deep fieldimage size by a factor of at least two; wherein placement of the flaskbetween the illuminator and camera coaxially and opposed on crossed,ultra precision mechanical stages driven by processing means, generatesrectangular arrays of continuous digital images, and; whereby thedensity of any one cell, for example an islet cell, is derived as afunction of scattering, absorption, and thickness.
 2. A system accordingto claim 1, further comprising, tissue volume analysis (TVA) software,using thresholding to identify individual cells, for example isletcells, and to evaluate cross-sectional linear size of cells, for exampleislet cells, via an algorithmic simulacrum of manual counting undermicroscopy.
 3. A system according to claim 2, wherein said TVA softwaremeasures the optical transmission of each pixel within each cell, forexample islet cell, using this to calculate thickness of tissue at thatpixel based upon optical extinction; and, based on constant pixel area,calculates volume represented by respective pixels; whereby voxels,volume elements, are then summed to generate resultory total cell, forexample islet cell, volume.
 4. A system according to claim 1, wherein anautomated image measurement algorithm is employed.
 5. A system accordingto claim 2, wherein an automated image measurement algorithm isemployed.
 6. A system according to claim 3, wherein an automated imagemeasurement algorithm is employed.
 7. A process for generating sizingand volume measurement data by transforming optical imagery, comprising,in combination: providing an AIMS/imaging apparatus/scanner; having TVAalgorithms and related software disposed therein; imaging at least acell; generating predetermined fields for sizing and volume; conforming,registering and (or otherwise validating) the resultory data; and, usingthe same to create information which can be further compared to lasertranssection microscopy and diffusing measurement data to confirmresulting data sets.
 8. A machine for transforming optical data intocellular sizing and volume measurement information, which, whereby amultiplicity of images are created using illumination spectra between atleast about 500 and 700 nm to generate a plurality of sliced scannedimages which are assembled based upon cross-sectional linear size usedto calculate cell volume for a population, further comprising at leastone of islets and spheres of stem cells; in combination with optionally,at least an independent unit for validating the same, whereby confocalmicroscopy, likewise capable of diffusivity measurement, enables opticalslicing of images of said islets and spheres of stem cells horizontally,using scattered light to document the subject area, which whenmultiplied by thickness, generates an independent validation of thevolume generated by said machine.
 9. The machine of claim 8, the isletcells being at least one population selected from the group of largeanimals, humans and swine.